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--- |
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tags: |
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- image-classification |
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license: mit |
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datasets: |
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- imagenet-1k |
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--- |
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See: https://huggingface.co/timm/edgenext_small.usi_in1k |
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```python |
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from urllib.request import urlopen |
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import einops |
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import numpy as np |
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import onnxruntime as ort |
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from PIL import Image |
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def softmax(x): |
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y = np.exp(x - np.max(x)) |
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return y / y.sum(axis=0) |
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IMG_URL = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' |
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IN1K_CLASSES_URL = 'https://storage.googleapis.com/bit_models/ilsvrc2012_wordnet_lemmas.txt' |
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session = ort.InferenceSession('edgenext_small.usi_in1k.ort') |
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# session = ort.InferenceSession('edgenext_small.usi_in1k.onnx') |
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labels = urlopen(IN1K_CLASSES_URL).read().decode().splitlines() |
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img = np.array( |
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Image.open(urlopen(IMG_URL)) |
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.resize(session._sess.inputs_meta[0].shape[2:]) |
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) |
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# e.g. in1k norm stats |
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mean = .485, .456, .406 |
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sd = .229, .224, .225 |
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img = (img / 255. - mean) / sd |
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# to clearly illustrate format ort expects |
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img = einops.rearrange(img, 'h w c -> 1 c h w').astype(np.float32) |
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out = session.run(None, {session.get_inputs()[0].name: img}) |
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out = softmax(out[0][0]) |
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topk = np.argsort(out)[::-1][:5] |
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for i in topk: |
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print(f'{out[i]:.2f}', labels[i]) |
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``` |
